Aerial imagery is a valuable tool in many industries. It can be useful in precision agriculture practices, mapping, surveillance, monitoring, etc. Satellite imagery can be used for these purposes, but aerial imagery taken from a manned or unmanned aircraft provides the benefits of higher-resolution, on-demand imagery. However, manned and unmanned aircraft both operate at much lower altitudes than satellites and cannot capture as large of an area in one image. This necessitates a map of the area of interest created from several images that have been stitched together into a mosaic. To make a complete, high-quality mosaic, it is vital that the images cover the full area of interest with sufficient overlap. The amount of image overlap and coverage over the area of interest can be difficult to ensure, especially during a mission. Lack of sufficient coverage typically is not recognized until after an attempt to process the imagery into a mosaic, a lengthy process that, depending on the resolution and number of images, can take many hours. If lack of coverage, non-sufficient overlap, or a bad image is discovered, the operator has to re-do the mission to collect the required imagery. This mission can be delayed due to weather or other considerations, or it can interfere with the scheduling of the operator's other missions. All of this postpones the delivery of the product and drives up data acquisition costs.
To determine the portions of the Earth's surface that are depicted in captured aerial images, it is necessary to georeference the captured images. Traditional methods of georeferencing involve matching ground control points from a base map with corresponding features found in the captured images. This method can be time consuming and may be impossible if there are no readily identifiable ground control points in the images. However, if geographic position and attitude of the camera at the time of image capture can be accurately associated with each captured image, direct georeferencing methods can be used to give the images geographic context in an automated fashion.
Current imaging systems may include various features, but are incapable of interfacing with a variety of cameras, do not allow for cameras to be triggered based on parameters other than a fixed time interval, do not provide an operators with the ability to visualize the mission in real-time to manage and verify the coverage and image overlap of their aerial imaging missions, and some have large and heavy components that limit the type of vehicles into which the system can be integrated.
There is need of a small, lightweight, integrated system that readily interfaces with a variety of cameras to intelligently control camera triggering, verify image capture, accurately associate images with geographic position data and attitude data representative of the camera at the time of image capture, perform direct georeferencing calculations for each captured image, and display a visualization of the mission and captured images over the area of interest in real-time. This visualization allows the user to verify that sufficient image coverage of the area of interest and overlap between captured images is accomplished during the imaging mission as well as provide a preview of the captured images over the area of interest. The ability to see image coverage and captured images during a mission makes imaging missions easier, more efficient, and less stressful for operators and allows them to immediately adapt for poorly tracked or inappropriately planned flight lines, maximizing the number of successful missions that they can perform in a day. Operators are also able to act on any information gleaned from the captured images while they are still at the field.